Data, Analytics & AI

Skill areas

Automation / RPA Analyst

An Automation / RPA Analyst identifies, designs, and supports the deployment of software robots and automated workflows that replace or augment repetitive manual processes in an organisation. Day-to-day work involves working with business teams to map current processes, assess their suitability for automation, document requirements for robotic process automation (RPA) tools such as UiPath, Automation Anywhere, or Blue Prism, supporting the configuration and testing of bots, and monitoring deployed automations to ensure they continue to function correctly as underlying systems change. The role sits at the intersection of business analysis and technical implementation — Automation Analysts must understand processes well enough to redesign them, and understand the tooling well enough to specify and validate solutions. Entry-level positions typically focus on process discovery, documentation, and testing support, progressing toward bot development and ownership of the automation pipeline. The function exists across financial services, healthcare, the public sector, utilities, and retail — anywhere manual, rule-based processes are consuming significant operational capacity. Organisations running large automation programmes often employ dedicated RPA analysts alongside a Centre of Excellence that sets standards and governs the pipeline. Analysts who develop both process redesign skills and hands-on RPA tool proficiency progress fastest, as the ability to bridge business and technical perspectives is the core value of the role.

Career information

Everything you need to know about this role

Salary, eligibility, background, qualifications, and where people typically work.

Expected salary range

£28,000–£45,000 (entry to mid-level, UK). Senior RPA Analysts and RPA Developers £45,000–£65,000. Automation Architects and Heads of RPA £65,000–£95,000+. Financial services and large public sector organisations at the higher end of each band. Contract day rates for experienced RPA developers range from £400–£700.

Eligibility limits

Right to work in the UK required. Public sector and NHS roles may require DBS checks. Government and defence automation roles may require SC clearance. No formal licence required for commercial roles. Some financial services automation roles may involve FCA-related screening given access to production operational systems.

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History and context

RPA emerged as a commercial discipline in the early 2010s, initially targeting back-office financial processes — invoice processing, data entry, account reconciliation — where rule-based repetition was consuming large volumes of analyst and operations time. Vendors including Blue Prism (founded in the UK), UiPath, and Automation Anywhere built GUI-based development environments that allowed bots to replicate human interactions with desktop and web applications without requiring API integration, dramatically lowering the barrier to automation. UK adoption accelerated through the mid-2010s, with financial services firms, NHS trusts, and central government bodies all running significant RPA programmes. The discipline has since broadened significantly. Intelligent automation — combining RPA with AI capabilities such as optical character recognition (OCR), natural language processing, and machine learning — enables automation of processes that involve unstructured data, judgement, or variability that pure rule-based RPA cannot handle. Hyperautomation — a term popularised by Gartner — describes the orchestration of multiple automation technologies into end-to-end process automation. Cloud-native automation platforms and low-code/no-code tools have expanded the automation toolkit further. The analyst role is evolving from pure RPA bot configuration toward broader process automation architecture, combining RPA, APIs, AI, and workflow tools to design the most appropriate solution for each process.

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History and context

Question 5 · #5

ScenarioMedium

How would you handle an exception in an automated process?

scenarioautomation-rpa-analyst

Why they ask

Exception handling is where most bot failures originate. Good exception design is the difference between a bot that runs reliably and one that crashes silently and causes downstream damage.

What they want

A structured approach covering exception types, how each is handled, what information is captured, and how the bot communicates with humans when it cannot proceed.

How to structure your answer

Distinguish business exceptions (expected edge cases) from system exceptions (unexpected failures)
Describe how business exceptions are handled — log, route to human queue, send notification
Describe how system exceptions are handled — retry logic, screenshot, alert, graceful stop
Explain the importance of logging and audit trails
Describe how exceptions are reviewed and used to improve the automation

Sample answer

I distinguish between two types of exception. A business exception is an expected variation — data that falls outside the normal range, a missing field, a case that the process rules say should be handled differently. These are anticipated in the design phase and the bot should handle them gracefully: log the case with enough detail to identify it, route it to a human queue or send a notification email with the relevant information, and continue processing the next item without stopping. A system exception is an unexpected failure — a system is unavailable, a UI element has changed, a timeout occurs. These need different handling: the bot should retry a defined number of times with a pause between attempts, and if the retries fail, it should capture a screenshot, log the error details, send an alert to the operations team, and stop processing that item without damaging the workflow state. The key principles are that the bot should never fail silently — every exception must be logged with enough context to diagnose and resolve it — and it should never leave data in a partial state without recording that fact. I would also build a regular exception review into the governance process, so that recurring business exceptions that the bot is not handling efficiently can be fed back into the process design.

Common mistakes

Treating all exceptions the same — business and system exceptions need different handling strategies
Not mentioning logging and audit trails — exception information that is not captured cannot be acted on
Describing exception handling as just "send an email" — the full handling logic including retry, screenshot, and graceful stop is what interviewers want to see

Bonus tips

Reference the retry scope and try-catch patterns used in UiPath or similar — they signal hands-on familiarity
Mention that exception queues — work items that the bot could not complete — should be monitored and acted on within defined SLAs
Note that the exception rate is a key operational metric for any automation — consistently high exception rates signal a process or bot design problem that needs addressing

Automation / RPA Analyst

Everything you need to know about this role

Expected salary range

Eligibility limits

History and context

Everything you need to know about this role

Expected salary range

Eligibility limits

History and context

Typical qualifications

Major employers

Where this role can take you

Overview

Progression opportunities

Typical timelines

Why this path matters now

Sample career paths

RPA developer and architect track

Related roles in this library

Career tips

Practise the questions you'll actually get asked

Tell me about yourself and why you want to work in automation and RPA.

What makes a process a good candidate for RPA automation?

Walk me through how you would document a process for automation.

What is the difference between attended and unattended automation?

How would you handle an exception in an automated process?

Describe a time you improved a process or identified an inefficiency.

Why should we hire you for this Automation / RPA Analyst role?

Digital transformation track